Lloyd's Market Data Analyst (Up to £800 per day)

Albion Blake
London
1 week ago
Applications closed

Related Jobs

View all jobs

Head of Data (Commercial Lines GCS)

Portfolio Optimisation Lead (Basé à London)

Strategic Finance Analyst

Strategic Finance Manager

Computational Fluid Dynamics Specialist

Head of Data Engineering

Job Title:Lloyd’s Market Data Analyst (Contract)

Location:Hybrid - London

Duration:6 months

Day rate:Up to £800 per day (outside IR35) depending on experience


We are seeking aData Analystto join aManaging General Agent (MGA) client of ours on a contract basis. This role will be responsible for data cleansing, data modelling and support an impending data migration


Key skills

  • Extensive experience as a Data Analyst, within a Managing General Agent (MGA)
  • Strong proficiency in SQL, Excel, Power BI and Python
  • Solid understanding of data migration, data modelling, data cleansing, and data visualisation
  • Strong analytical skills and the ability to interpret complex data and provide actionable insights
  • Familiarity with insurance data (policies, claims, underwriting)
  • Familiarity with insurance-specific software and systems (e.g., underwriting platforms, claims management systems)
  • Excellent communication skills, with the ability to present complex data in a clear and concise manner to both technical and non-technical audiences
  • Experience with statistical analysis and data mining techniques
  • Bachelor’s degree in data science, Statistics, Mathematics, Economics, or a related field


Key Responsibilities:

  • Data Collection & Management:Gather and structure large sets of data from various internal and external sources. Ensure data quality, integrity, and accuracy across all datasets.
  • Data Analysis & Reporting:Analyse business data and generate actionable insights to improve processes, enhance operational efficiency, and support business strategy. Create ad-hoc and regular reports using advanced Excel, SQL, and Power BI.
  • Data Modelling & Visualisation: Build and maintain data models to identify trends, forecasts, and business opportunities. Design and implement dashboards and visualizations to present findings to key stakeholders.
  • Data Migration:Plan, execute and oversee the migration of data from legacy systems to new platforms. Ensure integrity, accuracy, and security of data throughout the migration process. Collaborate with technical teams, business stakeholders, and IT departments to deliver seamless, on-time migration.
  • Business Support:Collaborate with business teams, including underwriting, claims, and operations, to understand their data needs and provide analytical support for strategic decision-making.
  • Compliance & Documentation:Ensure all data-related processes and reports comply with internal policies and regulatory requirements, especially in the context of the insurance industry.


Due to the nature of the client, candidates without Lloyd's market experience will not be considered for this role.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.

Machine Learning Programming Languages for Job Seekers: Which Should You Learn First to Launch Your ML Career?

Machine learning has swiftly become a cornerstone of modern technology, transforming entire industries—healthcare, finance, e-commerce, and beyond. As a result, demand for machine learning engineers, data scientists, and ML researchers continues to surge, creating a rich landscape of opportunity for job seekers. But if you’re new to the field—or even an experienced developer aiming to transition—the question arises: Which programming language should you learn first for a successful machine learning career? From Python and R to Scala, Java, C++, and Julia, the array of choices can feel overwhelming. Each language boasts its own community, tooling ecosystem, and industry use cases. This detailed guide, crafted for www.machinelearningjobs.co.uk, will help you align your learning path with in-demand machine learning roles. We’ll delve into the pros, cons, and ideal use cases for each language, offer a simple starter project to solidify your skills, and provide tips for leveraging the ML community and job market. By the end, you’ll have the insights you need to confidently pick a language that catapults your machine learning career to new heights.